Overview of Deep Lake's Index implementation for ANN search.
How Deep Lake Implements an Index for ANN Search
Deep Lake implements the Hierarchical Navigable Small World (HSNW) index for Approximate Nearest Neighbor (ANN) search. The index is based on the OSS Hsnwlib package with added optimizations. The implementation enables users to run queries on >35M embeddings in less than 1 second.
Unique aspects of Deep Lake's HSNW implementation
Rapid index creation with multi-threading optimized for Deep Lake
Efficient memory management that reduces RAM usage
Memory Management in Deep Lake
RAM Cost >> On-disk Cost >> Object Storage Cost
Minimizing RAM usage and maximizing object store significantly reduces costs of running a Vector Database. Deep Lake has a unique implementation of memory allocation that minimizes RAM requirement without any performance penalty:
Memory Architecture for the Deep Lake Vector Store
Using the Index
By default, the index is turned off in Deep Lake. To enable the index, during Vector Store initialization or loading, specify the Vector Store length threshold above which the index will be applied: